Extreme clinical phenotypes caused by rare genetic variations have been the basis of some of the most exciting drug targets in today’s pharmaceutical pipeline: PCSK9 in cholesterol, sclerostin in bone diseases, Nav1.7 for pain. The rare gene signatures underlying these programs can provide a big boost to the confidence in rationale (CIR) for working on novel drug approaches, especially those that have no clinical precedent for pharmacologic intervention.

Target selection is one of the biggest drivers of risk for a young discovery-stage startup or a similarly-staged project team in Pharma. Numerous analyses in the past show a striking difference in attrition with clinically precedented vs novel targets; in short, chasing targets with best-in-class profiles has historically been more successful than first-in-class novel approaches. But in today’s world, where the demand and requirement for more transformative innovation is much higher than in the past, understanding how to better prosecute those novel targets has become critical.

This is where understanding extreme phenotypes and the impact of “Nature’s Experiments” can greatly help on target prioritization and interrogation; since target validation is a hugely important risk driver, and any insight enabling one to increase, or decrease, CIR can be of real value.

There are obviously different types of signals in genetics. Broadly speaking, variations like SNPs or deletions can lead to either gain of function or loss of function impacts (e.g., more/less signaling, enhanced/repressed expression, increased/decreased enzyme activity). The most profound gains or losses in function are the rare and most instructive phenotypes, and these have been the subject of much discussion (see this great paper by Sasha Kamb, Sean Harper, and Kari Stefansson at Amgen/DeCode, as well as this David Shaywitz piece).

Another excellent paper last fall from Robert Plenge, Ed Scolnick, and David Altshuler at the Broad Institute, explores the topic further, including “dose response” curves derived from Nature’s Experiments emphasizing its usefulness as tool to pick and prioritize targets. The authors explore the use of a ‘genetic therapeutic index’ of specific targets – where Nature has shown both the efficacy and safety effect of knocking out or augmenting a protein’s activity. For those interested in a thorough discussion of the topic, both of the above papers are well worth reading.

Although common variants and their cumulative impact on disease risk and health are important, the rare or less frequent tails of this gain/loss of function distribution are where drug hunters searching for CIR insight are focused.

These alterations in physiologic activity, when compared to “normal” individuals, can manifest as either protective, positive phenotypes (improved health) or as adverse phenotypes (e.g., risk factors or disease drivers). The chart below illustrates these differences, and highlights some of the well-known examples in medicine today. Each of these quadrants obviously has different implications for how to think about the therapeutic opportunity.

Where a loss-of-function looks protective, like Nav1.7, Nature would suggest antagonizing those channels would be beneficial in “normal” individuals suffering from pain. Similarly, where a gain-of-function is driving a disease, like many cancer mutations, therapeutics to antagonize those functions would likely be beneficial; further, in that quadrant, the genetics can also enable patient stratification and selection.

Importantly, target validation can extend to the more general concept of “pathway validation”: rare variants up and down a signaling cascade can highlight phenotypic impact of interrogating a broader pathway of biology. For instance, blocking wnt family members involved with sclerostin in different bone disorders.

If approached correctly, leveraging these risk-reducing insights from nature to help prioritize “target selection” can help an early drug discovery program improve its odds of seeing a clinically meaningful outcome in 3-6 years. As we handicap the risk profiles of the 100s of drug discovery stage startup opportunities we see each year, being mindful of Nature’s experiments in genetics can help us get a better handle on the likelihood a target is worth pursuing.

Because we are a firm focused in large part on drug discovery, target selection is a critical variable to get right, and we apply these whatever insights we can glean from genetics into target prioritization across our portfolio. While not all of the ~60 or so drug programs active in our companies fall into the “tails” of the distribution above, a reasonable percentage have strong genetic evidence. Here are a dozen or so diverse examples meant to illustrate the breadth of the approach:

Increased copy number of SMN2 correlates with less severe clinical outcomes in spinal muscular atrophy patients, supports that up-regulation of SMN2 expression would be beneficial (ref): RaNA Therapeutics

These types of targets are certainly not unique to Atlas; other early stage VCs invest in companies and targets with similar human genetic profiles. For example, Third Rock Venture’s portfolio has a number of targets validated through “tail” variations in gene function: Agios’ inborn error of metabolism program acround PKR, MyoKardia’s focus on myosin in hypertrophic cardiomyopathy, Global Blood’s aim to augment mutant hemoglobin’s oxygen binding, etc… Similar portfolios can presumably be found elsewhere in the early stage venture community.

Smart target selection is half the battle against project attrition, if not more. Although there are any number of ways for drug candidates to fail for reasons other than the target’s role in the disease, finding drug matter against a target is a more “manageable” risk than chasing irrelevant biology, or so says this former molecular biologist…

The absence of CETP from this discussion is rather noticeable. Its a poignant example of how the genetics may essentially prove the hypothesis, but turning that into a functioning drug is quite a different task.

George Donnelly

Nature’s experiment is also called evolution – why are you shy about it!

munchma cuchi

Naturally, the classic example is cancer, where the extremely mutagenic microenvironment drives all sorts of mutations, which are then selected for if they enhance growth and metastasis.

Caught22

Thanks Bruce for all the work you put into this post—reference lists along with the Atlas Co’s is really useful. One issue for putting this into practice in the “real world” is that established co’s have indications they want to go into—those spaces don’t always come along with ‘nature’s experiments’ to suggest targets. For NewCo generation, you can just follow the biology, regardless of the indication.